1. Field of the Invention
Embodiments of the present invention generally relate to the construction, analysis, and characterization of dynamical states of biological networks at the cellular level. Methods are provided for analyzing the dynamical states by constructing matrices using high-throughput data types, such as fluxomic, metabolomic, and proteomic data. Some embodiments relate to an individual, while others relate to a plurality of individuals.
2. Description of the Related Art
There have been significant advances in the development of statistical analysis of genome-scale networks (Slonim, 2002), that have been propelled by the availability of genome-scale high-throughput datasets and the successes of constraint-based modeling approaches (Kummel et al., 2006; Palsson, 2006; Pharkya et al., 2004; Price et al., 2004; Reed et al., 2006). The foundation of such genome-scale analysis is built upon the descriptions of the biochemical transformations in a network in a self-consistent and chemically accurate matrix format. Much progress has been made with the genome-scale network reconstruction process, and a growing number of genome-scale metabolic reconstructions are now available (Feist et al., 2007; Jamshidi and Palsson, 2007; Oh et al., 2007; Reed et al., 2006; Resendis-Antonio et al., 2007).
Reconstructions of genome-scale biochemical reaction networks (Reed et al., 2006) have been analyzed by topological (Barabasi and Oltvai, 2004) and constraints-based (Price et al., 2004) methods, but dynamic models, at this scale, have been unachievable to date. It turns out that the matrix representation of the biochemical transformations in a network are not only a requisite for dynamic models but also a major determinant in their properties, and thus, it is important to have well curated reconstructions available. The growing availability of metabolomic and fluxomic data sets (Breitling et al., 2007; Goodacre et al., 2004; Hollywood et al., 2006; Sauer, 2006) and methods to estimate the thermodynamic properties (Henry et al., 2007; Henry et al., 2006; Mavrovouniotis, 1991) of biochemical reactions has opened up the possibility to formulate large-scale kinetic models.
The growing amount of disparate data sets (including proteomic, fluxomic, and metabolomic) has produced the unmet need to integrate and analyze this data in a functional context that is tailored to specific individuals. The availability of annotated genomes enabled the reconstruction of genome-scale networks, and now the availability of high-throughput metabolomic and fluxomic data along with thermodynamic information opens the possibility to build genome-scale kinetic models. This invention describes a framework for building and analyzing such models, thus satisfying those needs.